Jurassic.pl (侏逻辑)

Run Julia codes in Prolog.

Prolog is a powerful logic programming language designed for automating first-order logical reasoning.
Julia is a highly-efficient Python-like programming language designed for data science, machine learning and scientific domains.

Naming of this software: "JUlia in LOGIC programming" ⇒ (translation) "茱莉娅 + 逻辑程序" ⇒ (acronym) "茱逻辑" ⇒ (Mandarin pronunciation) "zhū luó jì" ⇒ (pronounce) "侏罗纪" ⇒ (translate) "Jurassic".

Prerequisites

Install SWI-Prolog and Julia, make sure swipl-ld and julia is in your PATH.

SWI-Prolog (tested with swipl-devel)
Julia (tested with julia-1.7.1)

This package is only tested on Linux, not sure if it will compile on MacOS (maybe) or Windows (very unlikely).

Remark: In some cases, the julia and swipl on your machine may be compiled with different libm or libgmp, which could cause munmap_chunk(): invalid pointer error when using Jurassic.pl. For now, this problem is difficult to fix, a quick workaround is running swipl and Jurassic.pl with the default libc.so, for example:

LD_PRELOAD=/usr/lib/libc.so.6 swipl jurassic.pl

To make it simpler, I usually add an alias in ~/.bashrc (or any rc of your shell) by

alias pl='LD_PRELOAD=/usr/lib/libc.so.6 swipl'

After then, just call pl jurassic.pl instead. (Please see this issue)

UPDATE: Another workaround is compiling swipl-devel with USE_TCMALLOC=OFF.

Build

Just run make.

make

To debug the package, please uncomment the #define JURASSIC_DEBUG in c/jurassic.h.

Usage

Load jurassic module in SWI-Prolog:

?- use_module(jurassic).

%% or consult jurassic.pl directly.
?- ['jurassic.pl'].

Julia Expressions

Call Julia expressions in Prolog with symbol :=:

?- := println("Hello World!").
% output
Hello World!
true.

?- a := sqrt(2.0),
     := println(a).
1.4142135623730951
true.

Find out if an atom has been defined in the embedded Julia:

?- jl_isdefined(a).
false.

?- a := 1.
true.

?- jl_isdefined(a).
true.

Julia Functions

Define a function and call a Julia macro:

?- := f(x) = x*transpose(x).
true.

?- := @show(f([1,2,3,4,5])).
f([1, 2, 3, 4, 5]) = [1 2 3 4 5; 2 4 6 8 10; 3 6 9 12 15; 4 8 12 16 20; 5 10 15 20 25]
true.

Array element references by []:

?- a := f([1,2,3,4,5]).
true.

?- X = 2, := @show(a[1,X]).
a[1, 2] = 2
X = 2.

?- X = 100, := @show(a[1,X]).
BoundsError: attempt to access 5×5 Array{Int64,2} at index [1, 100]
false.

Define more complex functions with command strings by predicate cmd/1:

?- := cmd("fib(n) = n <= 1 ? 1 : fib(n-1) + fib(n-2)").
true.

?- := @time(@show(fib(46))).
fib(46) = 2971215073
  7.567550 seconds (3.97 k allocations: 228.485 KiB)
true.

Multiple lines also work:

?- := cmd("function fib2(n)
         n <= 1 && return 1
         sum = 0
         while n > 1
             sum += fib2(n-1)
             n -= 2
         end
         return sum + 1
     end").
true.

?- := @time(@show(fib2(46))).
fib2(46) = 2971215073
  4.409316 seconds (60.55 k allocations: 3.183 MiB)
true.

Similar program in Prolog (without tabling for memorising facts) takes much more time:

fib_pl(N, 1) :-
    N =< 1, !.
fib_pl(N, X) :-
    N1 is N-1, fib_pl(N1, X1),
    N2 is N-2, fib_pl(N2, X2),
    X is X1 + X2.

% Prolog without tabling
?- time(fib_pl(40, X)).
% 827,900,701 inferences, 55.618 CPU in 55.691 seconds (100% CPU, 14885532 Lips)
X = 165580141.

% Julia in Jurassic
?- := @time(@show(fib(40))).
fib(40) = 165580141
  0.525090 seconds (3.97 k allocations: 228.626 KiB)
true.

?- := @time(@show(fib2(40))).
fib2(40) = 165580141
  0.296593 seconds (6.86 k allocations: 392.581 KiB)
true.

Using Julia Packages

Import Julia packages or source files:

?- jl_using("Flux").
true.

?- jl_include("my_source_file.jl").
true.

Jurassic.pl also supports Julia's 'Package'.function field accessing. The 'Package' is quoted as a Prolog atom, otherwise uppercase words are treated as Prolog variables.

?- jl_using("Pkg").
true.

?- := 'Pkg'.status().
    Status `~/.julia/environments/v1.2/Project.toml`
  [336ed68f] CSV v0.5.12
  [159f3aea] Cairo v0.6.0
  [324d7699] CategoricalArrays v0.6.0
  ...
true.

It also works with struct fields as well:

?- := "struct point
           x
           y
       end".
true.

?- a := cmd("[point([1,2,3],[1,2,3]), point([2,3,4],[2,3,4])]").
true.

?- := @show(a[2].x[1]).
(a[2]).x[1] = 2
true.

Julia `ccall`

One of the most fascinating features of Julia is it can call C/Fortran functions directly from shared libraries, i.e., the ccall function (use QuoteNode via $x) (link). Jurassic.pl also supports it:

% wrapping tuples with predicate tuple/1, passing datatypes (Int32) as atoms:
?- := @show(ccall(tuple([$clock, "libc.so.6"]), 'Int32', tuple([]))).
ccall((:clock, "libc.so.6"), Int32, ()) = 1788465
true.

You can also use strings to define ccall functions, just remember to use escape character \" to pass string arguments:

?- := cmd("function getenv(var::AbstractString)
    val = ccall((:getenv, \"libc.so.6\"),
                Cstring, (Cstring,), var)
    if val == C_NULL
        error(\"getenv: undefined variable: \", var)
    end
    unsafe_string(val)
end").
true.

?- X := getenv("SHELL").
X = "/bin/zsh".

Interaction Between Prolog and Julia

Unify Prolog terms with Julia expressions:

?- := f(x) = sqrt(x) + x^2 + log(x) + 1.0.
true.

?- X := f(2.0).
X = 7.10736074293304.

% Prolog and Julia work together
?- between(1,10,X),
   Y := f(X).
X = 1,
Y = 3.0 ;
X = 2,
Y = 7.10736074293304 ;
X = 3,
Y = 12.830663096236986 ;
...

The unification from Julia side will first try to access the value of Julia symbol if it is defined variable; if failed, treat the symbol name as an atom:

?- a := 1,
   X := a,
   b := X + 1,
   Y := b,
   Z := c.
X = 1,
Y = 2,
Z = c.

Currently, the unification only works for 1d-arrays:

?- := f(x) = pi.*x.
true.

?- X := f([1,2,3,4,5]).
X = [3.141592653589793, 6.283185307179586, 9.42477796076938, 12.566370614359172, 15.707963267948966].

Unification of (>1)d-arrays will fail:

?- := f(x) = x*transpose(x).
true.

?- := @show(typeof(f([1,2,3]))),
   X := f([1,2,3]).
typeof(f([1, 2, 3])) = Array{Int64,2}
[ERR] Cannot unify list with matrices and tensors!
false.

`Rational` numbers

Both Prolog and Julia supports rational numbers, which are more accurate and useful than floating numbers in many applications. Following is an example converting rational numbers from both ends.

?- A := [100//200, 3//2, 19//20].
A = [1r2, 3r2, 19r20].

?- a := [100r200, 3r2, 19r20], := @show(a).
a = Rational{Int64}[1//2, 3//2, 19//20]

Using rational number together with SparseArrays:

?- jl_using('SparseArrays').
true.

?- I = 1, a := spzeros('Rational', 4), a[I] := 1//2, A := 'Array'(a).
I = 1,
A = [1r2, 0, 0, 0].

?- I = 1, a := spzeros('Rational', 4, 4), a[I,I] := 1//2, c := a*transpose(a), := display('Array'(c)).
4×4 Matrix{Rational}:
 1//4  0//1  0//1  0//1
 0//1  0//1  0//1  0//1
 0//1  0//1  0//1  0//1
 0//1  0//1  0//1  0//1
I = 1.

`QuoteNode` and `Symbol`

QuoteNode and Symbol are different in Julia. QuoteNode is an AST node that won't get evaluated (stored as Expr), while Symbol will be accessed when calling jl_toplevel_eval in Julia's main module.

Remark: In Jurassic.pl, symbols (:x) will be parsed as Symbol only; for QuoteNode usage, you should use $x (or $(x)). When unifying a Prolog term and a Julia atom with ":=/2", Symbol will be evaluated automatically; while unification with "=/2" won't evaluate symbols. For example, in the Tuple example, to define a tuple contains symbols:

?- a := tuple([2.0, $'I\'m a quoted symbol']),
     := @show(a).
a = (2.0, Symbol("I''m a quoted symbol"))
true.

% wrong usage
?- a := tuple([2.0, :'I\'m a quoted symbol will be evaluated']),
     := @show(a).
UndefVarError: I''m a quoted symbol  will be evaluated not defined
false.

?- a := array('Int64', undef, 2, 2).
true.

?- X = a, := @show(X).
a = [139986926082480 139986777391200; 139986805220544 0]
X = a.

?-  X = :a, := @show(X).
a = [139986926082480 139986777391200; 139986805220544 0]
X = :a.

Julia Constants and Keywords

Julia constants as atoms, e.g. Inf, missing, nothing, etc.:

% Jurassic.pl
?- X := 1/0.
X = inf.

?- X := -1/0.
X = ninf.

% Prolog
?- X is 1/0.
ERROR: Arithmetic: evaluation error: `zero_divisor'
ERROR: In:
ERROR:   [10] _6834 is 1/0
ERROR:    [9] <user>

Tuples are defined with Prolog predicate tuple/1, whose argument is a list:

?- a := tuple([1,"I'm string!", tuple([2.0, $'I\'m a quoted symbol'])]),
     := @show(a).
a = (1, "I'm string!", (2.0, Symbol("I'm a quoted symbol")))
true.

Tuples are useful for returning multiple values, when it appears on the left hand side of :=, the atoms in tuples are treated as Julia variables, and variables are for unification. This is useful when Julia functions return data in multiple formats, for example, a linear regression model (a struct) with its r2 score (Float64) on training data:

?- := @show(a).
UndefVarError: a not defined
false.

?- := cmd("f(x) = (x, x^2, x^3)").
true.

?- A = a, tuple([A, B, C]) := f(-2).
A = a,
B = 4,
C = -8.

?- := @show(a).
a = -2
true.

Remark: Symbols in lists and tuples are always evaluated before :=/2 unification, and their behaviours are different:

?- a:=1.
true.

?- X := [a, :a, $a, :(:a), $($a)].
X = [1, 1, $ (:a), $ (:a), $ ($ (:a))].

%% behaviour of tuple is different to list
?- X := tuple([a, :a, $a, :(:a), $($a)]).
X = tuple([1, 1, :a, :a, $ (:a)]).

%% nothing will change under =/2 unification
?- X = [a, :a, $a, :(:a), $($a)].
X = [a, :a, $a, : (:a), $ ($a)].

?- X = tuple([a, :a, $a, :(:a), $($a)]).
X = tuple([a, :a, $a, : (:a), $ ($a)]).

Keyword assignments in a function call are represented by the kw/2 predicate:

% Plot with Jurassic.pl using Plots.jl
?- jl_using("Plots").
true.
% Use backend GR
?- := gr().
true.
% Use kw/2 to assign values to keywords,
% This command is equals to Julia command:
%     "plt = plot(rand(10), title = "10 Random numbers", fmt = :png, show = false))".
?- plt := plot(rand(10), kw(title, "10 Random numbers"), kw(fmt, :png), kw(show, false)).
true.
% Save the plot.
?- := savefig(plt, "rand10.png").
true.

Plotted image:

Spreads arguments with ...:

?- := foo(x,y,z) = sum([x,y,z]).
true.

?- := foo([1,2,3]).
MethodError: no method matching foo(::Array{Int64,1})
Closest candidates are:
  foo(::Any, !Matched::Any, !Matched::Any) at none:0
false.

?- := foo([1,2,3]...).
true.

Anonymous functions

Anonymous functions are useful in second-order functions. Jurassic.pl use operator ->> instead of -> because the latter one is the condition operator in Prolog. Following is an example of using ->>:

?- X := map(x ->> pi*x, [1,2,3,4,5]).
X = [3.141592653589793, 6.283185307179586, 9.42477796076938, 12.566370614359172, 15.707963267948966].

Meta-Programming

Julia supports meta-programming, expressions Expr(head, arg1, arg2, arg3) are represented with predicate jl_expr/2, in which the first argument is the predicate (root on the AST), the second argument contains all the arguments of the expression (leaves on the AST). For example, a Julia express 1+a has AST:

julia> dump(Meta.parse("1+a"))
Expr
  head: Symbol call
  args: Array{Any}((3,))
    1: Symbol +
    2: Int64 1
    3: Symbol a

In Jurassic.pl, it will be represented as jl_expr(:call, [+, 1, :a]) or jl_expr(:call, [:(+), 1, :a]). However, because ":=/2" will automatically evaluate symbols in the expressions, directly assigning an atom with Expr will cause errors:

?- jl_isdefined(a).
false.

?- e := jl_expr(:call, [+, 1, :a]).
UndefVarError: a not defined
false.

%% if a is defined
?- a := 2, 
   e := jl_expr(:call, [+, 1, :a]).
true.

%% instead of assigning a Expr, it assigns the evaluated results
?- := @show(e).
e = 3
true.

To assign a variable with value of Julia Expr via :=/2, please use the QuoteNode operator $:

?- e := $jl_expr(:call, [+, 1, :a]).
true.

%% explicitly claim "+" is a symbol with :/1
?- e := $jl_expr(:call, [:(+), 1, :a]).
true.

?- := 'Meta'.show_sexpr(e), 
   nl.
(:call, :+, 1, :a)
true.

%% evaluate the expression e (will fail because a is not defined)
?- := @show(eval(e)).
UndefVarError: a not defined
false.

%% define a
?- a := 2.
true.

%% evaluate the expression e (success)
?- := @show(eval(e)).
eval(e) = 3
true.

%% assign the evaluated results of e to new variable b
?- b := eval(e),
   := @show(b).
b = 3
true.

To simplify the expression construction, Jurassic.pl provides a predicate Y $= X for Y := $(X):

?- e $= jl_expr(:call, [:(+), :a, 2]), 
   := @show(e),
   := 'Meta'.show_sexpr(e), 
   := @show(eval(e)).
% @show(e)
e = :(a + 2)
% Meta.show_sexpr(e)
(:call, :+, :a, 2)
% @show(eval(e))
eval(e) = 4
true.

Moreover, jl_expr/2 can be used for constructing nested AST expressions:

?- X1 = jl_expr(:call, [:(+), :a, 2]),
   X2 = jl_expr(:call, [:(*), X1, :a]),
   := @show(eval(X2)).
eval((a + 2) * a) = 8
X1 = jl_expr(:call, [: (+), :a, 2]),
X2 = jl_expr(:call, [: (*), jl_expr(:call, [: (+), :a, 2]), :a]).

Construct expressions directly with `QuoteNode`

If no Prolog inferences are required when constructing an expression, we could assert it directly with QuoteNode assignment $=/2:

?- dxdt := array('Expr', undef, 2).
true.

%% construct directly with $=/2
?- dxdt[1] $= ((:a + :b)/2)^3.
true.

%% construct as a Prolog term
?- X = jl_expr(:call, [:(^), jl_expr(:call, [:(/), jl_expr(:call, [:(+), :a, :b]), 2]), 3]),
   dxdt[2] $= X.
true.

?- := @show(dxdt).
dxdt = Expr[:(((a + b) / 2) ^ 3), :(((a + b) / 2) ^ 3)]
true.

?- := dxdt[1] == dxdt[2].
true.

Unify Prolog terms with Julia `Expr`

Prolog terms can also unify with Julia AST expressions:

?- a $= jl_expr(:call, [+, 1, :b]).
true.

?- X := a.
X = jl_expr(:call, [: (+), 1, :b]).

?- jl_expr(H, A) := a.
H = :call,
A = [: (+), 1, :b].

%% unify with nested expressions
?- X1 = jl_expr(:call, [:(+), :a, 2]),
    e $= jl_expr(:call, [:(*), X1, :a]).
X1 = jl_expr(:call, [: (+), :a, 2]).

?- X := e, writeln(X).
jl_expr(:call,[: (*),jl_expr(:call,[: (+),:a,2]),:a])
X = jl_expr(:call, [: (*), jl_expr(:call, [: (+), :a, 2]), :a]).

Create Julia function from Prolog

The expressions defined by jl_expr/2 can be used for constructing Julia functions by jl_declare_function/3, whose basic usage is:

jl_declare_function(Function_Name, Function_Arguments, Function_Codes).

The arguments are:

Function_Name is a Julia Symbol, it is represented by an Prolog atom, e.g., myfunc or :myfunc.
Function_Arguments is an array of Julia Symbol, it is represented by an Prolog list of atoms, e.g., [x, y] or [:x, :y]. Note that Julia does NOT take quoted symbols (QuoteNode) as valid function name and arguments, so $myfunc and [$x,$y] won't work.
Function_Codes is a Julia code block ((:block, expr1, expr2, ...)), which is represented by a list of jl_expr/2 in Prolog.

Following is an example from DifferentialEquations.jl, in which a 3-D Lorenz system is declared and solved numerically.

The Julia code of the parameterized Lorenz function looks like this:

function parameterized_lorenz!(du,u,p,t)
    du[1] = p[1]*(u[2]-u[1])
    du[2] = u[1]*(p[2]-u[3]) - u[2]
    du[3] = u[1]*u[2] - p[3]*u[3]
end

Assuming that we have already constructed the differential equations by Prolog inference and stored them in a list [E1, E2, E3], we can declare the function by jl_declare_function('parameterized_lorenz!', [:du, :u, :p, :t], [E1, E2, E3]). Following is a full example:

solve_lorenz :-
    % import useful libraries
    jl_using('DifferentialEquations'),
    jl_using('Plots'),
    
    % construct julia equations by Prolog 
    E1 = jl_expr(: (=),[jl_expr(:ref,[:du,1]),jl_expr(:call,[: (*),jl_expr(:ref,[:p,1]),jl_expr(:call,[: (-),jl_expr(:ref,[:u,2]),jl_expr(:ref,[:u,1])])])]),
    E2 = jl_expr(: (=),[jl_expr(:ref,[:du,2]),jl_expr(:call,[: (-),jl_expr(:call,[: (*),jl_expr(:ref,[:u,1]),jl_expr(:call,[: (-),jl_expr(:ref,[:p,2]),jl_expr(:ref,[:u,3])])]),jl_expr(:ref,[:u,2])])]),
    E3 = jl_expr(: (=),[jl_expr(:ref,[:du,3]),jl_expr(:call,[: (-),jl_expr(:call,[: (*),jl_expr(:ref,[:u,1]),jl_expr(:ref,[:u,2])]),jl_expr(:call,[: (*),jl_expr(:ref,[:p,3]),jl_expr(:ref,[:u,3])])])]),
    
    % declare Julia function
    jl_declare_function('parameterized_lorenz!', [:du, :u, :p, :t], [E1, E2, E3]),
    
    % set parameters
    u0 := [1.0,0.0,0.0],
    tspan := tuple([0.0,100.0]),
    p := [10.0,28.0,8/3],
    prob := 'ODEProblem'('parameterized_lorenz!',u0,tspan,p),
    
    % solve the problem and plot the result
    sol := solve(prob),
    := plot(sol, kw(vars,tuple([1,2,3])), kw(show,true)).

It will generate a nice figure like this:

Alternatively, the Function_Codes argument can be an atom referring to a Julia array of Exprs:

solve_lorenz_alt :-
    % import useful libraries
    jl_using('DifferentialEquations'),
    jl_using('Plots'),
    
    % define equations with Julia
    e := array('Any', undef, 3),
    e[1] $= du[1] = p[1]*(u[2]-u[1]),
    e[2] $= du[2] = u[1]*(p[2]-u[3]) - u[2],
    e[3] $= du[3] = u[1]*u[2] - p[3]*u[3],
    
    % declare Julia function
    jl_declare_function('parameterized_lorenz!', [:du, :u, :p, :t], e),
    ...

Arrays

Array can be initialised with function array, which is equal to Array{Type, Dim}(Init, Size) in Julia:

?- a := array('Float64', undef, 2, 2, 2).
true.

?- := @show(a[1,:,:]).
a[1, :, :] = [2.5e-322 0.0; 0.0 0.0]
true.

New arrays can also be initialised with a Prolog predicate jl_new_array/4 predicates:

% jl_new_array(Name, Type, Init, Size) works like Name = Array{Type, Dim}(Init, Size)
% in Julia, here Size is a list.
?- jl_new_array(a, 'Int', undef, [2, 2, 2]).
true.

?- := @show(a[1,:,:]).
a[1, :, :] = [34359738371 77309411345; 64424509449 140320876527618]
true.

Array initialisation also supports type unions with predicate union(Type1, Type2, ...):

?- a := array(union('Int64', 'Missing'), missing, 2, 2).
true.

?- a[1, :] := [1,2].
true.

?- := @show(a).
a = Union{Missing, Int64}[1 2; missing missing]
true.

Unifying Prolog list with multi-dimension arrays

Added a callable predicate jl_unify_arrays/0 to enable multi-dimension arrays, for example:

?- X := zeros(2,2,2).
[ERR] Cannot unify list with matrices and tensors!
false.

?- jl_unify_arrays.
true.

?- X := zeros(2,2,2).
X = [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]].

?- a := rand(2,2,2), 
   := display(a[1,:,:]),
   := display(a[2,:,:]),
   X := a. % unification
2×2 Matrix{Float64}:
 0.985283  0.552947
 0.545242  0.243907
2×2 Matrix{Float64}:
 0.0608626  0.056837
 0.0653907  0.354753
X = [[[0.98528283223325, 0.552946822654699], 
      [0.5452419312034834, 0.24390678160983548]], 
     [[0.060862609653857036, 0.056837009536256256], 
      [0.0653907190522306, 0.3547534277306833]]].

After enabling multi-dimension arrays, they could be unified with Prolog's nested lists:

?- jl_using('SparseArrays').
true.

?- a := sparse(zeros('Rational', 2,2)),
   a[1,1] := 1//2,
   := display(a),
   X := 'Array'(a*transpose(a)).
2×2 SparseMatrixCSC{Rational, Int64} with 1 stored entry:
 1//2   ⋅
  ⋅     ⋅
X = [[1r4, 0], [0, 0]].

Assigning a multi-dimension array with nested Prolog list via array/1 functor:

?- X = [[1,2],[3,4]],
   a := array(X), 
   := display(a).
2×2 Matrix{Int64}:
 1  2
 3  4
X = [[1, 2], [3, 4]].

Array unification problem

Jurassic seems cannot handle long array unification currently. The best workaround is to store and process large tensors in Julia.

?- X := zeros(10,10,10).
X = [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0|...], [0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0|...], [0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0|...], [0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0|...], [0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0, 0.0, 0.0|...], [0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0|...], [...|...]|...], [[...|...]|...], [...|...]|...].

?- X := zeros(100,100,10).
X = [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0|...], [0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0|...], [0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0, 0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0|...], [0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0, 0.0, 0.0, 0.0|...], [0.0, 0.0, 0.0|...], [0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0, 0.0, 0.0|...], [0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0, 0.0|...], [0.0|...], [...|...]|...], [[0.0|...], [...|...]|...], [[...|...]|...], [...|...]|...].

?- X := zeros(100,100,100).
false.

TODO

More features to be added, e.g.:

Multi-threading.

Compile and test code in other platform, e.g.:

MaxOS
Windows

Acknowledgement

The Jurassic.pl package is inspired by real (calling R from Prolog).

Another similar package is pljulia, unfortunately it is deprecated and only has limited functionalities.

Author

Wang-Zhou Dai (homepage)
School of Intelligence Science and Technology,
Nanjing University, Suzhou Campus

Name		Name	Last commit message	Last commit date
Latest commit History 143 Commits
c		c
julia		julia
lib		lib
.ccls		.ccls
.gitignore		.gitignore
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
jurassic.pl		jurassic.pl
rand10.png		rand10.png
test.pl		test.pl

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Jurassic.pl (侏逻辑)

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Usage

Julia Expressions

Julia Functions

Using Julia Packages

Julia `ccall`

Interaction Between Prolog and Julia

`Rational` numbers

`QuoteNode` and `Symbol`

Julia Constants and Keywords

Anonymous functions

Meta-Programming

Construct expressions directly with `QuoteNode`

Unify Prolog terms with Julia `Expr`

Create Julia function from Prolog

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Unifying Prolog list with multi-dimension arrays

Array unification problem

TODO

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Jurassic.pl (侏逻辑)

Prerequisites

Build

Usage

Julia Expressions

Julia Functions

Using Julia Packages

Julia ccall

Interaction Between Prolog and Julia

Rational numbers

QuoteNode and Symbol

Julia Constants and Keywords

Anonymous functions

Meta-Programming

Construct expressions directly with QuoteNode

Unify Prolog terms with Julia Expr

Create Julia function from Prolog

Arrays

Unifying Prolog list with multi-dimension arrays

Array unification problem

TODO

Acknowledgement

Author

About

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Contributors 2

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Julia `ccall`

`Rational` numbers

`QuoteNode` and `Symbol`

Construct expressions directly with `QuoteNode`

Unify Prolog terms with Julia `Expr`

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